SOTAVerified

Sarcasm Detection

The goal of Sarcasm Detection is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.

Source: Attentional Multi-Reading Sarcasm Detection

Papers

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Showing 176–200 of 266 papers

TitleStatusHype
Detecting Sarcasm in Conversation Context Using Transformer-Based Models—0
Context-Aware Sarcasm Detection Using BERT—0
Sarcasm Detection Using an Ensemble Approach—0
Sarcasm Identification and Detection in Conversion Context using BERT—0
C-Net: Contextual Network for Sarcasm Detection—0
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis—0
A Transformer Approach to Contextual Sarcasm Detection in Twitter—0
Transformers on Sarcasm Detection with Context—0
Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection—0
A Novel Hierarchical BERT Architecture for Sarcasm Detection—0
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks—0
Sarcasm Detection in Tweets with BERT and GloVe Embeddings—0
Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context—0
Sarcasm Detection using Context Separators in Online Discourse—0
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze BehaviourCode0
Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media—0
A Report on the 2020 Sarcasm Detection Shared Task—0
Urban Dictionary Embeddings for Slang NLP Applications—0
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset—0
An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data—0
A Transformer-based approach to Irony and Sarcasm detectionCode0
Deep and Dense Sarcasm DetectionCode0
iSarcasm: A Dataset of Intended Sarcasm—0
Cross-Cultural Transfer Learning for Text Classification—0
Exploring Author Context for Detecting Intended vs Perceived Sarcasm—0
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All datasetsBIG-bench (SNARKS)FigLang 2020 Reddit DatasetFigLang 2020 Twitter DatasetSARC (all-bal)SARC (pol-bal)iSarcasmMUStARDSARC (pol-unbal)WITS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM 2(few-shot, k=3, CoT)Accuracy84.8—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy78.7—Unverified
3PaLM 540B (few-shot, k=3)Accuracy78.1—Unverified
4BLOOM 176B (few-shot, k=3)Accuracy72.47—Unverified
5Bloomberg GPT (few-shot, k=3)Accuracy69.66—Unverified
6GPT-NeoX (few-shot, k=3)Accuracy62.36—Unverified
7Chinchilla-70B (few-shot, k=5)Accuracy58.6—Unverified
8Gopher-280B (few-shot, k=5)Accuracy48.3—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT+Aspect-based approachesF10.74—Unverified
2RoBERTa_large - (Separated Context-Response)F10.72—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa_large (Context-Response)F10.77—Unverified
2BERTF10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1CASCADEAccuracy77—Unverified
2Bag-of-BigramsAccuracy75.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-BigramsAccuracy76.5—Unverified
2CASCADEAccuracy74—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa + Mutation Data AugmentationF1-Score0.41—Unverified
#ModelMetricClaimedVerifiedStatus
1MUStARD++Precision70.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-WordsAvg F127—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTR136.88—Unverified